{"id":"W7117455766","doi":"10.1007/s11883-025-01380-1","title":"Toward Precision Medicine in Atherosclerotic Cardiovascular Disease: Insights from Omics Data into Sex Differences","year":2025,"lang":"en","type":"article","venue":"Current Atherosclerosis Reports","topic":"Sex and Gender in Healthcare","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"HORIZON EUROPE Marie Sklodowska-Curie Actions; Agenzia Spaziale Italiana; Ministarstvo Prosvete, Nauke i Tehnološkog Razvoja; European Commission","keywords":"Omics; Precision medicine; Systems biology; Metabolomics; Personalized medicine; Genomics; Human genetics; Variety (cybernetics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005506116,0.001172917,0.003466177,0.003071522,0.0005301213,0.003232044,0.001393904,0.002229287,0.003962795],"category_scores_gemma":[0.01063918,0.0003698367,0.001679525,0.003010947,0.001434245,0.003093134,0.002223043,0.00369507,0.001190179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214607,"about_ca_system_score_gemma":0.004825431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001121511,"about_ca_topic_score_gemma":0.001362409,"domain_scores_codex":[0.9981169,0.0006602369,0.0002763334,0.0003235095,0.0005022348,0.0001209286],"domain_scores_gemma":[0.9882719,0.008376166,0.0009762801,0.0003394187,0.001691492,0.0003447355],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002004688,0.0000368771,0.001900724,0.06534628,0.001457292,0.0004903218,0.0004316482,0.001050086,0.002730454,0.02514975,0.05424548,0.8469607],"study_design_scores_gemma":[0.0000316659,0.0001358898,0.003614473,0.03479626,0.001318511,0.001375623,0.0004318233,0.0005628344,0.001268114,0.03194038,0.924432,0.00009241144],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002210398,0.9909618,0.001871959,0.005237077,0.0009479573,0.00001118824,0.0001472247,0.00003540824,0.0005663114],"genre_scores_gemma":[0.002281403,0.9912652,0.00164888,0.002819285,0.001562604,0.00002425461,0.0001789371,0.0000127465,0.0002067377],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.005506116,"threshold_uncertainty_score":0.02911943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1670973318569289,"score_gpt":0.3597640224263798,"score_spread":0.1926666905694509,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}